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ML-Like Inference for Classifiers

  • Cristiano Calcagno
  • Eugenio Moggi
  • Walid Taha
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2986)

Abstract

Environment classifiers were proposed as a new approach to typing multi-stage languages. Safety was established in the simply-typed and let-polymorphic settings. While the motivation for classifiers was the feasibility of inference, this was in fact not established. This paper starts with the observation that inference for the full classifier-based system fails. We then identify a subset of the original system for which inference is possible. This subset, which uses implicit classifiers, retains significant expressivity (e.g. it can embed the calculi of Davies and Pfenning) and eliminates the need for classifier names in terms. Implicit classifiers were implemented in MetaOCaml, and no changes were needed to make an existing test suite acceptable by the new type checker.

Keywords

Type System Type Inference Code Fragment Principal Type Linear Time Temporal Logic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Cristiano Calcagno
    • 1
  • Eugenio Moggi
    • 2
  • Walid Taha
    • 3
  1. 1.Imperial CollegeLondonUK
  2. 2.DISI, Univ. of GenovaItaly
  3. 3.Rice UniversityUSA

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